Datasets for event-based Human Pose Estimation.
Few datasets have been recorded using event-cameras, especially if compared with the huge amount of RGB datasets. For human pose estimation, Calabrese released DHP19, a dataset with recordings of 17 subjects and 33 movements. On the other hand, simulating events from RGB videos is a promising path of research, especially since multiple works proved the soundness of training on simulated events. Mueggleret al. [1]} generate synthetic events from RGB images and compare real and synthetic events for ego-pose estimation in various scenarios. More recently, simulated events have been employed for image reconstruction [2]}, depth estimation [3]}, and motion segmentation [4]}, especially in high-speed scenarios where RGB ground-truth are hard to collect. In this work, we propose a pipeline to generate synthetic events from the Human3.6m dataset [5]}, [6]} and compare our approach with standard RGB methods to establish a strong benchmark for further research.
